Improving Decision Sparsity

TL;DR

This paper enhances the concept of decision sparsity in machine learning explanations by introducing flexible, cluster-based, and tree-based SEV methods, along with algorithms to optimize sparse, meaningful explanations aligned with decision-making.

Contribution

It extends the Sparse Explanation Value (SEV) framework to produce more relevant and sparser explanations, incorporating clustering, tree structures, and optimization algorithms.

Findings

More meaningful decision explanations achieved

Cluster-based and tree-based SEV improve explanation relevance

Algorithms effectively optimize decision sparsity

Abstract

Sparsity is a central aspect of interpretability in machine learning. Typically, sparsity is measured in terms of the size of a model globally, such as the number of variables it uses. However, this notion of sparsity is not particularly relevant for decision-making; someone subjected to a decision does not care about variables that do not contribute to the decision. In this work, we dramatically expand a notion of decision sparsity called the Sparse Explanation Value(SEV) so that its explanations are more meaningful. SEV considers movement along a hypercube towards a reference point. By allowing flexibility in that reference and by considering how distances along the hypercube translate to distances in feature space, we can derive sparser and more meaningful explanations for various types of function classes. We present cluster-based SEV and its variant tree-based SEV, introduce a method that improves credibility of explanations, and propose algorithms that optimize decision sparsity in machine learning models.